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Rahul Bhattacharya

Bio: Rahul Bhattacharya is an academic researcher from West Bengal State University. The author has contributed to research in topics: Covariate & Bayesian probability. The author has an hindex of 7, co-authored 14 publications receiving 127 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper attempts to explore the available response-adaptive randomization procedures together with a comparison of their performances, and some real-life adaptive trial is reviewed.
Abstract: A variety of response-adaptive randomization procedures have been proposed in literature assuming binary outcomes. However, the list is not so long for continuous outcomes though many real clinical trials deal with continuous treatment responses. In this paper, we attempt to explore the available procedures together with a comparison of their performances. Some real-life adaptive trial is also reviewed.

26 citations

Journal ArticleDOI
TL;DR: A randomized two-stage adaptive Bayesian design is proposed and studied for allocation and comparison in a phase III clinical trial with survival time as treatment response and the applicability of the proposed methodology is illustrated.
Abstract: A randomized two-stage adaptive Bayesian design is proposed and studied for allocation and comparison in a phase III clinical trial with survival time as treatment response. Several exact and limiting properties of the design and the follow-up inference are studied, both numerically and theoretically, and are compared with a single-stage randomized procedure. The applicability of the proposed methodology is illustrated by using some real data.

21 citations

Journal ArticleDOI
TL;DR: A randomized two-stage adaptive design for allocation of patients to treatments and comparison in a phase III clinical trial with survival time as treatment responses and the possibility of several covariates is considered.
Abstract: A randomized two-stage adaptive design is proposed and studied for allocation of patients to treatments and comparison in a phase III clinical trial with survival time as treatment responses. We consider the possibility of several covariates in the design and analysis. Several exact and limiting properties of the design and the follow-up inference are studied, both numerically and theoretically. The applicability of the proposed methodology is illustrated by using some real data.

17 citations

Journal Article
TL;DR: In this article, a randomized two-stage adaptive design is proposed and studied for allocation of patients to treatments and comparison in a phase III clinical trial with survival time as treatment responses.
Abstract: A randomized two-stage adaptive design is proposed and studied for allocation of patients to treatments and comparison in a phase III clinical trial with survival time as treatment responses. We consider the possibility of several covariates in the design and analysis. Several exact and limiting properties of the design and the follow-up inference are studied, both numerically and theoretically. The applicability of the proposed methodology is illustrated by using some real data.

16 citations

Journal ArticleDOI
TL;DR: In this paper, a generalized framework is proposed to derive multi-treatment optimal response-adaptive designs for phase-III clinical trials, and a detailed performance study is provided for three treatment trials minimising failures.
Abstract: Response-adaptive designs are used in phase III clinical trials to allocate a larger number of patients to the better treatment. Optimal response-adaptive designs have become popular in recent days for this purpose, where the design is derived from some optimal viewpoints, mostly by optimizing some objective function subject to some constraint(s). However, most of the optimal designs are derived with two treatments and only a few works are available for several treatments. The present paper provides a generalized framework to derive multi-treatment optimal response-adaptive designs. A detailed performance study is provided for three treatment trials minimising failures. The applicability is also judged by redesigning some real clinical trials.

13 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, survival distributions for reliability applications in the Biomedical Sciences are discussed, with a focus on the reliability of the distribution of survival distributions in the field of bio-medical applications.
Abstract: (1976). Survival Distributions: Reliability Applications in the Biomedical Sciences. Technometrics: Vol. 18, No. 4, pp. 501-501.

513 citations

Journal ArticleDOI
TL;DR: A flexible method of extending a study based on conditional power, where the significance of the treatment difference at the planned end is used to determine the number of additional observations needed and the critical value necessary after accruing those additional observations.

252 citations

Book ChapterDOI
01 Jan 2008

75 citations

Journal ArticleDOI
TL;DR: The objective of this paper is to review several important new classes of adaptive randomization procedures and convey information on the recent developments in the literature on this topic.
Abstract: In February 2010, the U.S. Food and Drug Administration (FDA, 2010) drafted guidance that discusses the statistical, clinical, and regulatory aspects of various adaptive designs for clinical trials. An important class of adaptive designs is adaptive randomization, which is considered very briefly in subsection VI.B of the guidance. The objective of this paper is to review several important new classes of adaptive randomization procedures and convey information on the recent developments in the literature on this topic. Much of this literature has been focused on the development of methodology to address past criticisms and concerns that have hindered the broader use of adaptive randomization. We conclude that adaptive randomization is a very broad area of experimental design that has important application in modern clinical trials.

72 citations

Journal ArticleDOI
TL;DR: The international scientific production of Bayesian clinical trials is described by investigating the actual development and use of Bayesian ‘adaptive’ methods in the setting of clinical trials.
Abstract: Adaptive or 'flexible' designs have emerged, mostly within frequentist frameworks, as an effective way to speed up the therapeutic evaluation process. Because of their flexibility, Bayesian methods have also been proposed for Phase I through Phase III adaptive trials; however, it has been reported that they are poorly used in practice. We aim to describe the international scientific production of Bayesian clinical trials by investigating the actual development and use of Bayesian 'adaptive' methods in the setting of clinical trials. A bibliometric study was conducted using the PubMed and Science Citation Index-Expanded databases. Most of the references found were biostatistical papers from various teams around the world. Most of the authors were from the US, and a large proportion was from the MD Anderson Cancer Center (University of Texas, Houston, TX). The spread and use of these articles depended heavily on their topic, with 3.1% of the biostatistical articles accumulating at least 25 citations within 5 years of their publication compared with 15% of the reviews and 32% of the clinical articles. We also examined the reasons for the limited use of Bayesian adaptive design methods in clinical trials and the areas of current and future research to address these challenges. Efforts to promote Bayesian approaches among statisticians and clinicians appear necessary.

52 citations